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Record W1984619920 · doi:10.2118/75162-ms

Improved Oil Sweep through Discrete Fracture Network Modeling of Gel Injections in the South Oregon Basin Field, Wyoming

2002· article· en· W1984619920 on OpenAlexaff
W. Dershowitz, Dawn Shuttle, Robert Parney

Bibliographic record

VenueSPE/DOE Improved Oil Recovery Symposium · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsWater injection (oil production)Petroleum engineeringStructural basinFracture (geology)GeologyForcing (mathematics)Oil fieldEnvironmental scienceGeotechnical engineeringGeomorphologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Abstract Gel injection is used with water injection in fractured and heterogeneous reservoirs to decrease water production and improve the oil sweep efficiency. Gel treatments are able to achieve this because the gel is preferentially deposited to the most conductive fractures, blocking the water flow through those structures, and forcing to water to displace oil from the rock matrix instead. This paper describes discrete fracture network (DFN) analyses and simulations of gel injection at the South Oregon Basin, Wyoming Phosphoria formation. These simulations were carried out to evaluate the efficacy of gel injection in improving water injection strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.205
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2002
Admission routes1
Has abstractyes

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